Geometric Epitope and Paratope Prediction.

MOTIVATION: Identifying the binding sites of antibodies is essential for developing vaccines and synthetic antibodies. In this paper, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the importance of geometric information. RESULTS: Specifically, we compare different geometric deep learning methods applied to proteins' inner (I-GEP) and outer (O-GEP) structures. We incorporate 3D coordinates and spectral geometric descriptors as input features to fully leverage the geometric information. Our research suggests that different geometrical representation information are useful for different tasks. Surface-based models are more efficient in predicting the binding of the epitope, while graph models are better in paratope prediction, both achieving significant performance improvements. Moreover we analyse the impact of structural changes in antibodies and antigens resulting from conformational rearrangements or reconstruction errors. Through this investigation, we showcase the robustness of geometric deep learning methods and spectral geometric descriptors to such perturbations. AVAILABILITY AND IMPLEMENTATION: The python code for the models and the processing pipeline is open-source and available at https://github.com/Marco-Peg/GEP. SUPPLEMENTARY INFORMATION: The supplementary material includes comprehensive details about the proposed method and additional results.

Scientific discovery in the age of artificial intelligence.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

Attentive Cross-Modal Paratope Prediction.

Antibodies are a critical part of the immune system, having the function of recognizing and mediating the neutralization of undesirable molecules (antigens) for future destruction. Being able to predict which amino acids belong to the paratope , the region on the antibody that binds to the antigen, can facilitate antibody engineering and predictions of antibody-antigen structures. The suitability of deep neural networks has recently been confirmed for this task, with Parapred outperforming all prior models. In this work, we first significantly outperform the computational efficiency of Parapred by leveraging à trous convolutions and self-attention. Second, we implement cross-modal attention by allowing the antibody residues to attend over antigen residues. This leads to new state-of-the-art results in paratope prediction, along with novel opportunities to interpret the outcome of the prediction.